modal-compute

name	modal-compute
description	Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available.

Modal Compute

Use the modal CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.

Setup

pip install modal
modal setup

Commands

Command	Description
modal run script.py	Run a script on Modal (ephemeral)
modal run --detach script.py	Run detached (background)
modal deploy script.py	Deploy persistently
modal serve script.py	Serve with hot-reload (dev)
modal shell --gpu a100	Interactive shell with GPU
modal app list	List deployed apps

GPU types

T4, L4, A10G, L40S, A100, A100-80GB, H100, H200, B200

Multi-GPU: "H100:4" for 4x H100s.

Script pattern

import modal

app = modal.App("experiment")
image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")

@app.function(gpu="A100", image=image, timeout=600)
def train():
    import torch
    # training code here

@app.local_entrypoint()
def main():
    train.remote()

When to use

• Stateless burst GPU jobs (training, inference, benchmarks)
• No persistent state needed between runs
• Check availability: command -v modal

/goal Inheritance

This skill inherits the SeaBridgeAI /goal default protocol. Frame the work with a persistent goal, Definition of Done, validation plan, risks, dependencies, expected artifacts, and completion evidence. Do not claim completion until the DoD is validated or a hard blocker is documented.

Canonical protocol: C:\Users\adelm\SeaBridgeAI\everything-claude-code\protocols\GOAL_PROTOCOL.md